docstr: Calculates the elementwise determinant of the log Jacobian
docstr_tok: ["Calculates","the","elementwise","determinant","of","the","log","Jacobian"]
code: def log_abs_det_jacobian(self, x, y):
      """
      Calculates the elementwise determinant of the log Jacobian
      """
      x_old, y_old = self._cached_x_y
      if x is not x_old or y is not y_old:
          # This call to the parent class Transform will update the cache
          # as well as calling self._call and recalculating y and log_detJ
          self(x)
  
      return self._cached_logDetJ
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.distributions import Transform, constraints
  from pyro.nn import DenseNN
  from ..conditional import ConditionalTransformModule
  from ..torch_transform import TransformModule
  from ..util import copy_docs_from
label_desc: Use image node_id 5 for calling the ConditionedRadial obj's underlying member method code with example usage: obj.log_abs_det_jacobian(x, y) and returns: self